An Online Multiple Instance Learning System for Semantic Image Retrieval

Zhang, Chengcui; Chen, Xin; Chen, Wei-Bang

doi:10.1109/ism.workshops.2007.23

Cited by 8 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ever since, it has increasingly been applied to a wide variety of tasks. Such as content-based information retrieval [24], drug discovery [25], pattern recognition [26], image classification [27], text classification [28], region-based image categorization [29], image annotation [30], object tracking [31] and time series prediction [22], to name a few. To illustrate the need for MIL, in the following we analyse how a multiple instance (MI) representation can be applied to image classification.…”

Section: Multiple Instance Learningmentioning

confidence: 99%

Multiple Instance Mamdani Fuzzy Inference

Khalifa

Frigui

2015

Int. J. Fuzzy Log. Intell. Syst.

View full text Add to dashboard Cite

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MIMamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

show abstract

Section: Multiple Instance Learningmentioning

confidence: 99%

Multiple Instance Mamdani Fuzzy Inference

Khalifa

Frigui

2015

Int. J. Fuzzy Log. Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…propose a method for single/multiple color extraction from multiple regions within an image [14]. O.Huseyin et al propose a method that segments the image so that the user can select different Regions of Interest [15]. The features of the regions are used for the efficient retrieval of similar images.…”

Section: Related Workmentioning

confidence: 99%

Multi-Query Content Based Image Retrieval System using Local Binary Patterns

Joseph¹,

Balakrishnan²

2011

IJCA

View full text Add to dashboard Cite

Content Based Image Retrieval systems open new research areas in Computer Vision due to the high demand of image searching methods. CBIR is the process of finding relevant image from large collection of images using visual queries. The proposed system uses multiple image queries for finding desired images from database. The different queries are connected using logical AND operation. Local Binary Pattern (LBP) texture descriptors of the query images are extracted and those features are compared with the features of the images in the database for finding the desired images. The proposed system is used for retrieving similar human face expressions. The use of multiple queries reduces the semantic gap between low level visual features and high level user expectation. The experimental result shows that, the use of multiple queries has better retrieval performance over single image queries.

show abstract

“…In another direction, [130] A common approach, pioneered by Billsus and Pazzani, is to decrease similarity among the items in the recommendation list by removing obvious items as well as very similar ones within the context of content-based RS [126]. Following the same direction, Zhang et al [133] presented a multi-objective function optimization based on similarity and diversity. They improved their approach by formulating a binary optimization problem to capture the trade-off between diversity and recommendation accuracy [134].…”

Section: Over-personalization and Concentration Bias Of Recommendationsmentioning

confidence: 99%

Peeking into the other half of the glass : handling polarization in recommender systems.

Badami¹

View full text Add to dashboard Cite

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item's content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to v keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated wi...

show abstract

An Online Multiple Instance Learning System for Semantic Image Retrieval

Cited by 8 publications

References 4 publications

Multiple Instance Mamdani Fuzzy Inference

Multiple Instance Mamdani Fuzzy Inference

Multi-Query Content Based Image Retrieval System using Local Binary Patterns

Peeking into the other half of the glass : handling polarization in recommender systems.

Contact Info

Product

Resources

About